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Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.
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Cross‐layered distributed data‐driven framework for enhanced smart grid cyber‐physical security
Abstract Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber‐threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real‐time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross‐layer approach. In this paper a cross‐layered strategy is presented, called Cross‐Layer Ensemble CorrDet with Adaptive Statistics(CECD‐AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter‐arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD‐AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1‐score compared to current approaches that only use SG measurement data for detection such as the traditional physics‐based State Estimation, ECD‐AS strategy and other machine learning classification‐based detection schemes.
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- Award ID(s):
- 1809739
- PAR ID:
- 10367597
- Publisher / Repository:
- DOI PREFIX: 10.1049
- Date Published:
- Journal Name:
- IET Smart Grid
- Volume:
- 5
- Issue:
- 6
- ISSN:
- 2515-2947
- Format(s):
- Medium: X Size: p. 398-416
- Size(s):
- p. 398-416
- Sponsoring Org:
- National Science Foundation
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